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Giles Hooker
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- affiliation: Cornell University, Ithaca, NY, USA
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2020 – today
- 2024
- [j21]Yichen Zhou, Zhengze Zhou
, Giles Hooker:
Approximation trees: statistical reproducibility in model distillation. Data Min. Knowl. Discov. 38(5): 3308-3346 (2024) - [j20]Yunzhe Zhou
A generic approach for reproducible model distillation. Mach. Learn. 113(10): 7645-7688 (2024) - [c12]Alexander Asemota, Giles Hooker:
Using Longitudinal Data for Plausible Counterfactual Explanations. HI-AI@KDD 2024: 8-17 - [c11]Jeremy Goldwasser, Giles Hooker:
Stabilizing Estimates of Shapley Values with Control Variates. xAI (2) 2024: 416-439 - [i19]Jeremy Goldwasser, Giles Hooker:
Provably Stable Feature Rankings with SHAP and LIME. CoRR abs/2401.15800 (2024) - [i18]Alexander Asemota, Giles Hooker:
Longitudinal Counterfactuals: Constraints and Opportunities. CoRR abs/2403.00105 (2024) - [i17]Xi Xin, Giles Hooker, Fei Huang:
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots. CoRR abs/2404.18702 (2024) - [i16]Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando Pérez, Per-Olof Persson, Christopher Rackauckas:
Differentiable Programming for Differential Equations: A Review. CoRR abs/2406.09699 (2024) - 2023
- [j19]Sarah Tan
Considerations when learning additive explanations for black-box models. Mach. Learn. 112(9): 3333-3359 (2023) - [i15]Jeremy Goldwasser, Giles Hooker:
Stabilizing Estimates of Shapley Values with Control Variates. CoRR abs/2310.07672 (2023) - 2022
- [j18]Yichen Zhou, Giles Hooker
Decision tree boosted varying coefficient models. Data Min. Knowl. Discov. 36(6): 2237-2271 (2022) - [j17]Yichen Zhou, Giles Hooker:
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution. J. Mach. Learn. Res. 23: 183:1-183:44 (2022) - [j16]Giles Hooker
Selecting the derivative of a functional covariate in scalar-on-function regression. Stat. Comput. 32(3): 35 (2022) - [i14]Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker:
The Infinitesimal Jackknife and Combinations of Models. CoRR abs/2209.00147 (2022) - [i13]Yunzhe Zhou, Peiru Xu, Giles Hooker:
A Generic Approach for Statistical Stability in Model Distillation. CoRR abs/2211.12631 (2022) - 2021
- [j15]Indrayudh Ghosal, Giles Hooker:
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate. J. Comput. Graph. Stat. 30(2): 493-502 (2021) - [j14]Zhengze Zhou, Lucas Mentch, Giles Hooker:
V-statistics and Variance Estimation. J. Mach. Learn. Res. 22: 287:1-287:48 (2021) - [j13]Giles Hooker
Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance. Stat. Comput. 31(6): 82 (2021) - [j12]Zhengze Zhou, Giles Hooker:
Unbiased Measurement of Feature Importance in Tree-Based Methods. ACM Trans. Knowl. Discov. Data 15(2): 26:1-26:21 (2021) - [c10]Zhengze Zhou, Giles Hooker, Fei Wang:
S-LIME: Stabilized-LIME for Model Explanation. KDD 2021: 2429-2438 - [i12]Lucas Mentch, Giles Hooker:
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning. CoRR abs/2102.12328 (2021) - [i11]Zhengze Zhou, Giles Hooker, Fei Wang:
S-LIME: Stabilized-LIME for Model Explanation. CoRR abs/2106.07875 (2021) - 2020
- [j11]Aurya Javeed
Timing observations of diffusions. Stat. Comput. 30(2): 405-417 (2020) - [c9]Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana:
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS 2020: 2402-2412 - [c8]Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells:
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. FODS 2020: 23-34
2010 – 2019
- 2019
- [i10]Zhengze Zhou, Giles Hooker:
Unbiased Measurement of Feature Importance in Tree-Based Methods. CoRR abs/1903.05179 (2019) - [i9]Giles Hooker, Lucas Mentch:
Please Stop Permuting Features: An Explanation and Alternatives. CoRR abs/1905.03151 (2019) - [i8]Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana:
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR abs/1911.04974 (2019) - [i7]Zhengze Zhou, Lucas Mentch, Giles Hooker:
Asymptotic Normality and Variance Estimation For Supervised Ensembles. CoRR abs/1912.01089 (2019) - 2018
- [j10]Yuefeng Wu, Giles Hooker
Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate. Entropy 20(12): 955 (2018) - [j9]Leifur Thorbergsson, Giles Hooker:
Experimental Design for Partially Observed Markov Decision Processes. SIAM/ASA J. Uncertain. Quantification 6(2): 549-567 (2018) - [j8]Giles Hooker, Lucas Mentch
Bootstrap bias corrections for ensemble methods. Stat. Comput. 28(1): 77-86 (2018) - [c7]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES 2018: 303-310 - [i6]Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo:
Transparent Model Distillation. CoRR abs/1801.08640 (2018) - [i5]Indrayudh Ghosal, Giles Hooker:
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate. CoRR abs/1803.08000 (2018) - [i4]Yichen Zhou
Approximation Trees: Statistical Stability in Model Distillation. CoRR abs/1808.07573 (2018) - 2017
- [j7]Chong Liu, Surajit Ray
Functional principal component analysis of spatially correlated data. Stat. Comput. 27(6): 1639-1654 (2017) - [c6]Keegan Kang
Control Variates as a Variance Reduction Technique for Random Projections. ICPRAM (Revised Selected Papers) 2017: 1-20 - [c5]Keegan Kang
Random Projections with Control Variates. ICPRAM 2017: 138-147 - [i3]Giles Hooker, Cliff Hooker:
Machine Learning and the Future of Realism. CoRR abs/1704.04688 (2017) - [i2]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR abs/1710.06169 (2017) - 2016
- [j6]Lucas Mentch, Giles Hooker:
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests. J. Mach. Learn. Res. 17: 26:1-26:41 (2016) - [j5]Giles Hooker, Steven Roberts:
Maximal autocorrelation functions in functional data analysis. Stat. Comput. 26(5): 945-950 (2016) - [c4]Keegan Kang
Improving the recovery of principal components with semi-deterministic random projections. CISS 2016: 596-601 - [i1]Hui Fen Tan, Giles Hooker, Martin T. Wells:
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. CoRR abs/1611.07115 (2016) - 2015
- [j4]Giles Hooker, Kevin K. Lin
Control Theory and Experimental Design in Diffusion Processes. SIAM/ASA J. Uncertain. Quantification 3(1): 234-264 (2015) - [j3]Mathew W. McLean
Restricted likelihood ratio tests for linearity in scalar-on-function regression. Stat. Comput. 25(5): 997-1008 (2015) - 2013
- [c3]Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker:
Accurate intelligible models with pairwise interactions. KDD 2013: 623-631 - 2012
- [j2]Giles Hooker, James O. Ramsay:
Learned-loss boosting. Comput. Stat. Data Anal. 56(12): 3935-3944 (2012) - [j1]Giles Hooker, Saharon Rosset:
Prediction-based regularization using data augmented regression. Stat. Comput. 22(1): 237-249 (2012)
2000 – 2009
Coauthor Index
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